Modeling and optimization of voltage and treatment time for electrocoagulation removal of hexavalent chromium

被引：69

作者：

Bhatti, Manpreet S. ^{[1
]}

Reddy, Akepati S. ^{[2
]}

Kalia, Rajeev K. ^{[3
]}

Thukral, Ashwani K. ^{[1
]}

机构：

[1] Guru Nanak Dev Univ, Dept Bot & Environm Sci, Amritsar 143005, Punjab, India

[2] Thapar Univ, Dept Biotechnol & Environm Sci, Patiala 147001, Punjab, India

[3] GGSS Thermal Power Plant, Control & Instrumentat Maintenance Circle, Rupnagar, Punjab, India

来源：

DESALINATION | 2011年 / 269卷 / 1-3期

关键词：

Cr(VI) removal; Statistical modeling; Response surface methodology; Artificial neural network; Desirability plot; Design-Expert software; RESPONSE-SURFACE METHODOLOGY; SYNTHETIC WASTE-WATER; ELECTROCHEMICAL TREATMENT; AQUEOUS-SOLUTION; BORON REMOVAL; CR(VI); DYE; ELECTRODES; ALUMINUM; WASTEWATERS;

D O I：

10.1016/j.desal.2010.10.055

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The present study was undertaken to optimize process variables, electrolysis voltage and treatment time for the electrocoagulation removal of hexavalent chromium (Cr(VI)). Response surface methodology (RSM) with center composite design was used to achieve energy efficient removal of Cr(VI). Predictive models using ANOVA and multiple response optimization revealed that optimal Cr(VI) removal efficiency occurred at 11 V and 18.6 min treatment time to give 50% Cr(VI) removal efficiency with a consumption of 15.46 KWh m(-3) energy. Multiple response optimization through a desirability function saves 32.3% energy consumption. The models explained 97.5% variability for Cr(VI) reduction efficiency and 99% variability for energy consumption. Artificial neural network (ANN) model was generated to validate the RSM predictions. Validation experiments were performed at proposed optimal conditions proved RSM and ANN predictions. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：157 / 162

页数：6

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